[jira] [Commented] (HIVE-15580) Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark

2017-01-25 Thread Ferdinand Xu (JIRA) 

[ 
https://issues.apache.org/jira/browse/HIVE-15580?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15837765#comment-15837765
 ] 

Ferdinand Xu commented on HIVE-15580:
-

Hi [~xuefuz], I am taking a leave because of Chinese new year. Will run some 
workloads to evaluate this patch once cluster is available.

> Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark
> -
>
> Key: HIVE-15580
> URL: https://issues.apache.org/jira/browse/HIVE-15580
> Project: Hive
>  Issue Type: Improvement
>  Components: Spark
>Reporter: Xuefu Zhang
>Assignee: Xuefu Zhang
> Fix For: 2.2.0
>
> Attachments: HIVE-15580.1.patch, HIVE-15580.1.patch, 
> HIVE-15580.2.patch, HIVE-15580.2.patch, HIVE-15580.3.patch, 
> HIVE-15580.4.patch, HIVE-15580.5.patch, HIVE-15580.patch
>
>
> Currently, orderBy (sortBy) and groupBy in Hive on Spark uses unbounded 
> memory. For orderBy, Hive accumulates key groups using ArrayList (described 
> in HIVE-15527). For groupBy, Hive currently uses Spark's groupByKey operator, 
> which has a shortcoming of not being able to spill to disk within a key 
> group. Thus, for large key group, memory usage is also unbounded.
> It's likely that this will impact performance. We will profile and optimize 
> afterwards. We could also make this change configurable.



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[jira] [Commented] (HIVE-15580) Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark

2017-01-24 Thread Xuefu Zhang (JIRA) 

[ 
https://issues.apache.org/jira/browse/HIVE-15580?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15836414#comment-15836414
 ] 

Xuefu Zhang commented on HIVE-15580:


Hi [~Ferd] and [~dapengsun], I'm wondering if you guys could help measure the 
performance impact of the patch here? We at Uber don't have a dedicated 
environment, so getting accurate measurement is challenging. It would be great 
if you guys can help. Based on the result, we may have some followup work to 
do. Thanks.

> Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark
> -
>
> Key: HIVE-15580
> URL: https://issues.apache.org/jira/browse/HIVE-15580
> Project: Hive
>  Issue Type: Improvement
>  Components: Spark
>Reporter: Xuefu Zhang
>Assignee: Xuefu Zhang
> Fix For: 2.2.0
>
> Attachments: HIVE-15580.1.patch, HIVE-15580.1.patch, 
> HIVE-15580.2.patch, HIVE-15580.2.patch, HIVE-15580.3.patch, 
> HIVE-15580.4.patch, HIVE-15580.5.patch, HIVE-15580.patch
>
>
> Currently, orderBy (sortBy) and groupBy in Hive on Spark uses unbounded 
> memory. For orderBy, Hive accumulates key groups using ArrayList (described 
> in HIVE-15527). For groupBy, Hive currently uses Spark's groupByKey operator, 
> which has a shortcoming of not being able to spill to disk within a key 
> group. Thus, for large key group, memory usage is also unbounded.
> It's likely that this will impact performance. We will profile and optimize 
> afterwards. We could also make this change configurable.



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[jira] [Commented] (HIVE-15580) Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark

2017-01-20 Thread Xuefu Zhang (JIRA) 

[ 
https://issues.apache.org/jira/browse/HIVE-15580?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15832383#comment-15832383
 ] 

Xuefu Zhang commented on HIVE-15580:


Thanks, Chao! I will commit this first and create a couple of followups. 
[~lirui], it would be great if you can also take a look at the patch. I will 
incorporate your comments (if any) as followups as well. Thanks.

> Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark
> -
>
> Key: HIVE-15580
> URL: https://issues.apache.org/jira/browse/HIVE-15580
> Project: Hive
>  Issue Type: Improvement
>  Components: Spark
>Reporter: Xuefu Zhang
>Assignee: Xuefu Zhang
> Attachments: HIVE-15580.1.patch, HIVE-15580.1.patch, 
> HIVE-15580.2.patch, HIVE-15580.2.patch, HIVE-15580.3.patch, 
> HIVE-15580.4.patch, HIVE-15580.5.patch, HIVE-15580.patch
>
>
> Currently, orderBy (sortBy) and groupBy in Hive on Spark uses unbounded 
> memory. For orderBy, Hive accumulates key groups using ArrayList (described 
> in HIVE-15527). For groupBy, Hive currently uses Spark's groupByKey operator, 
> which has a shortcoming of not being able to spill to disk within a key 
> group. Thus, for large key group, memory usage is also unbounded.
> It's likely that this will impact performance. We will profile and optimize 
> afterwards. We could also make this change configurable.



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[jira] [Commented] (HIVE-15580) Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark

2017-01-20 Thread Chao Sun (JIRA) 

[ 
https://issues.apache.org/jira/browse/HIVE-15580?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15832377#comment-15832377
 ] 

Chao Sun commented on HIVE-15580:
-

+1

> Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark
> -
>
> Key: HIVE-15580
> URL: https://issues.apache.org/jira/browse/HIVE-15580
> Project: Hive
>  Issue Type: Improvement
>  Components: Spark
>Reporter: Xuefu Zhang
>Assignee: Xuefu Zhang
> Attachments: HIVE-15580.1.patch, HIVE-15580.1.patch, 
> HIVE-15580.2.patch, HIVE-15580.2.patch, HIVE-15580.3.patch, 
> HIVE-15580.4.patch, HIVE-15580.5.patch, HIVE-15580.patch
>
>
> Currently, orderBy (sortBy) and groupBy in Hive on Spark uses unbounded 
> memory. For orderBy, Hive accumulates key groups using ArrayList (described 
> in HIVE-15527). For groupBy, Hive currently uses Spark's groupByKey operator, 
> which has a shortcoming of not being able to spill to disk within a key 
> group. Thus, for large key group, memory usage is also unbounded.
> It's likely that this will impact performance. We will profile and optimize 
> afterwards. We could also make this change configurable.



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[jira] [Commented] (HIVE-15580) Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark

2017-01-20 Thread Xuefu Zhang (JIRA) 

[ 
https://issues.apache.org/jira/browse/HIVE-15580?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15832194#comment-15832194
 ] 

Xuefu Zhang commented on HIVE-15580:


RB: https://reviews.apache.org/r/55776/

> Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark
> -
>
> Key: HIVE-15580
> URL: https://issues.apache.org/jira/browse/HIVE-15580
> Project: Hive
>  Issue Type: Improvement
>  Components: Spark
>Reporter: Xuefu Zhang
>Assignee: Xuefu Zhang
> Attachments: HIVE-15580.1.patch, HIVE-15580.1.patch, 
> HIVE-15580.2.patch, HIVE-15580.2.patch, HIVE-15580.3.patch, 
> HIVE-15580.4.patch, HIVE-15580.5.patch, HIVE-15580.patch
>
>
> Currently, orderBy (sortBy) and groupBy in Hive on Spark uses unbounded 
> memory. For orderBy, Hive accumulates key groups using ArrayList (described 
> in HIVE-15527). For groupBy, Hive currently uses Spark's groupByKey operator, 
> which has a shortcoming of not being able to spill to disk within a key 
> group. Thus, for large key group, memory usage is also unbounded.
> It's likely that this will impact performance. We will profile and optimize 
> afterwards. We could also make this change configurable.



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[jira] [Commented] (HIVE-15580) Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark

2017-01-20 Thread Hive QA (JIRA) 

[ 
https://issues.apache.org/jira/browse/HIVE-15580?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15831746#comment-15831746
 ] 

Hive QA commented on HIVE-15580:




Here are the results of testing the latest attachment:
https://issues.apache.org/jira/secure/attachment/12848310/HIVE-15580.5.patch

{color:green}SUCCESS:{color} +1 due to 1 test(s) being added or modified.

{color:red}ERROR:{color} -1 due to 13 failed/errored test(s), 10965 tests 
executed
*Failed tests:*
{noformat}
TestDerbyConnector - did not produce a TEST-*.xml file (likely timed out) 
(batchId=235)
org.apache.hadoop.hive.cli.TestCliDriver.testCliDriver[specialChar] (batchId=22)
org.apache.hadoop.hive.cli.TestEncryptedHDFSCliDriver.testCliDriver[encryption_join_with_different_encryption_keys]
 (batchId=159)
org.apache.hadoop.hive.cli.TestHBaseNegativeCliDriver.testCliDriver[cascade_dbdrop]
 (batchId=226)
org.apache.hadoop.hive.cli.TestHBaseNegativeCliDriver.testCliDriver[generatehfiles_require_family_path]
 (batchId=226)
org.apache.hadoop.hive.cli.TestMiniLlapCliDriver.testCliDriver[orc_ppd_basic] 
(batchId=135)
org.apache.hadoop.hive.cli.TestMiniLlapCliDriver.testCliDriver[orc_ppd_schema_evol_3a]
 (batchId=136)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[escape1] 
(batchId=139)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[escape2] 
(batchId=154)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[offset_limit_ppd_optimizer]
 (batchId=151)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[schema_evol_text_vec_part]
 (batchId=149)
org.apache.hadoop.hive.cli.TestNegativeMinimrCliDriver.testCliDriver[cluster_tasklog_retrieval]
 (batchId=87)
org.apache.hadoop.hive.cli.TestNegativeMinimrCliDriver.testCliDriver[minimr_broken_pipe]
 (batchId=87)
{noformat}

Test results: https://builds.apache.org/job/PreCommit-HIVE-Build/3065/testReport
Console output: https://builds.apache.org/job/PreCommit-HIVE-Build/3065/console
Test logs: http://104.198.109.242/logs/PreCommit-HIVE-Build-3065/

Messages:
{noformat}
Executing org.apache.hive.ptest.execution.TestCheckPhase
Executing org.apache.hive.ptest.execution.PrepPhase
Executing org.apache.hive.ptest.execution.ExecutionPhase
Executing org.apache.hive.ptest.execution.ReportingPhase
Tests exited with: TestsFailedException: 13 tests failed
{noformat}

This message is automatically generated.

ATTACHMENT ID: 12848310 - PreCommit-HIVE-Build

> Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark
> -
>
> Key: HIVE-15580
> URL: https://issues.apache.org/jira/browse/HIVE-15580
> Project: Hive
>  Issue Type: Improvement
>  Components: Spark
>Reporter: Xuefu Zhang
>Assignee: Xuefu Zhang
> Attachments: HIVE-15580.1.patch, HIVE-15580.1.patch, 
> HIVE-15580.2.patch, HIVE-15580.2.patch, HIVE-15580.3.patch, 
> HIVE-15580.4.patch, HIVE-15580.5.patch, HIVE-15580.patch
>
>
> Currently, orderBy (sortBy) and groupBy in Hive on Spark uses unbounded 
> memory. For orderBy, Hive accumulates key groups using ArrayList (described 
> in HIVE-15527). For groupBy, Hive currently uses Spark's groupByKey operator, 
> which has a shortcoming of not being able to spill to disk within a key 
> group. Thus, for large key group, memory usage is also unbounded.
> It's likely that this will impact performance. We will profile and optimize 
> afterwards. We could also make this change configurable.



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[jira] [Commented] (HIVE-15580) Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark

2017-01-20 Thread Hive QA (JIRA) 

[ 
https://issues.apache.org/jira/browse/HIVE-15580?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15831579#comment-15831579
 ] 

Hive QA commented on HIVE-15580:




Here are the results of testing the latest attachment:
https://issues.apache.org/jira/secure/attachment/12848310/HIVE-15580.5.patch

{color:green}SUCCESS:{color} +1 due to 1 test(s) being added or modified.

{color:red}ERROR:{color} -1 due to 13 failed/errored test(s), 10965 tests 
executed
*Failed tests:*
{noformat}
TestDerbyConnector - did not produce a TEST-*.xml file (likely timed out) 
(batchId=235)
org.apache.hadoop.hive.cli.TestCliDriver.testCliDriver[specialChar] (batchId=22)
org.apache.hadoop.hive.cli.TestEncryptedHDFSCliDriver.testCliDriver[encryption_join_with_different_encryption_keys]
 (batchId=159)
org.apache.hadoop.hive.cli.TestHBaseNegativeCliDriver.testCliDriver[cascade_dbdrop]
 (batchId=226)
org.apache.hadoop.hive.cli.TestHBaseNegativeCliDriver.testCliDriver[generatehfiles_require_family_path]
 (batchId=226)
org.apache.hadoop.hive.cli.TestMiniLlapCliDriver.testCliDriver[orc_ppd_basic] 
(batchId=135)
org.apache.hadoop.hive.cli.TestMiniLlapCliDriver.testCliDriver[orc_ppd_schema_evol_3a]
 (batchId=136)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[escape1] 
(batchId=139)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[escape2] 
(batchId=154)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[schema_evol_text_vec_part]
 (batchId=149)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[vector_varchar_simple]
 (batchId=152)
org.apache.hadoop.hive.cli.TestNegativeMinimrCliDriver.testCliDriver[cluster_tasklog_retrieval]
 (batchId=87)
org.apache.hadoop.hive.cli.TestNegativeMinimrCliDriver.testCliDriver[minimr_broken_pipe]
 (batchId=87)
{noformat}

Test results: https://builds.apache.org/job/PreCommit-HIVE-Build/3063/testReport
Console output: https://builds.apache.org/job/PreCommit-HIVE-Build/3063/console
Test logs: http://104.198.109.242/logs/PreCommit-HIVE-Build-3063/

Messages:
{noformat}
Executing org.apache.hive.ptest.execution.TestCheckPhase
Executing org.apache.hive.ptest.execution.PrepPhase
Executing org.apache.hive.ptest.execution.ExecutionPhase
Executing org.apache.hive.ptest.execution.ReportingPhase
Tests exited with: TestsFailedException: 13 tests failed
{noformat}

This message is automatically generated.

ATTACHMENT ID: 12848310 - PreCommit-HIVE-Build

> Eliminate unbounded memory usage for orderBy and groupBy in Hive on Spark
> -
>
> Key: HIVE-15580
> URL: https://issues.apache.org/jira/browse/HIVE-15580
> Project: Hive
>  Issue Type: Improvement
>  Components: Spark
>Reporter: Xuefu Zhang
>Assignee: Xuefu Zhang
> Attachments: HIVE-15580.1.patch, HIVE-15580.1.patch, 
> HIVE-15580.2.patch, HIVE-15580.2.patch, HIVE-15580.3.patch, 
> HIVE-15580.4.patch, HIVE-15580.5.patch, HIVE-15580.patch
>
>
> Currently, orderBy (sortBy) and groupBy in Hive on Spark uses unbounded 
> memory. For orderBy, Hive accumulates key groups using ArrayList (described 
> in HIVE-15527). For groupBy, Hive currently uses Spark's groupByKey operator, 
> which has a shortcoming of not being able to spill to disk within a key 
> group. Thus, for large key group, memory usage is also unbounded.
> It's likely that this will impact performance. We will profile and optimize 
> afterwards. We could also make this change configurable.



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